Shafali Jain et al. / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES Vol No. 1, Issue No. 1, 023  029
A NonParametric Approach for Performance Assessment of Generation Utilities in India Tripta Thakur, Associate Professor, Electrical Department, MANITBhopal, India462003 tripta_thakur@yahoo.co.in,
Abstract— The technical efficiency of 30 Indian state owned
through the introduction of competition and improvement of efficiency, but did not proceed as it planned. At this stage it is essential to have documentation of the effects of such reforms. Such documentation has been done in developed countries, however from a few case studies: the experience of developing countries remains much less researched. This documentation can be made by performance evaluation for the structural change in electric power industry. We will be able to find out the direction of the structural change in electric power industry in India by analyzing the efficiency level of power generation companies in India. Such a review of performance of existing utilities is a need for the success of any reform program. Based on efficiency analysis, benchmarks can be set, and targets for improvement may be identified. The efficiency evaluation is also necessary for generating competition and for sector regulation.
A
ES
Generation Utilities were investigated using Data Envelopment Analysis for the time period 200708. The above study provides the efficiency scores of electric utility so that they can rank themselves, identify their shortcomings, set targets, and try to achieve these targets. Input variables are: installed capacity, coal consumption, oil consumption, auxiliary consumption and energy losses and outputs are energy generated and Energy sold. In addition, slack evaluation and target evaluation for input variable has been carried out. The average overall efficiency is 84.83 % and nearly one third of the utilities lie below this average level. The above studies provides the scope for the improvement of internal efficiency of the state owned Generation Utilities which is always win to win situation for the utilities and consumers and especially relevant to the India as it needs addition in electricity generation to meet the growing demand.
Arun Shandilya, Professor, Electrical Department, MANITBhopal India462003 arunshandilya@yahoo.com
T
Shafali Jain, Research scholar, Electrical Department, MANITBhopal, India462003 shafalijain9@yahoo.co.in,
IJ
Index Terms—Data Envelopment Analysis (DEA), Stateowned Generation Utilities, Efficiency score, Slack analysis.
I. INTRODUCTION
S
ince the early 1980’s, many countries have implemented
electricity sector reforms. The main objective is to improve the efficiency of the sector even though the organization of the power sectors and the approaches to reform vary across the countries. The electric power industry which had been maintained as a vertically integrated system in the past, the restructuring of electric power industry in many countries in the world has been performed in the way so as to raise efficiency by introducing competition [3]. The restructuring of electric power industry in India kept pace with the worldwide trend and started with the purpose of decreasing the electricity price and to bridge the demandsupply gap
ISSN: 22307818
This efficiency evaluation can be through by a number of approaches. Among many possible efficiency measurement methods, DEA is one method that has been used especially for the complicated systems with lots of inputs and outputs for benchmarking since its introduction by Charnes, Cooper and Rhodes in 1978 based on previous work by Farrell on production efficiency. This paper presents a case study which provides efficiency scores of generation utilities for the year 200708, so that they can rank themselves, identify their shortcomings, set targets and tries to achieve those targets. In addition, slack evaluation and target evaluation for input variable has been carried out. II. METHODOLOGY DEA has been applied to calculate efficiency of different types of DMUs including schools, hospitals and power plants etc. A DMU is an entity, which we measure the efficiency levels, to be compared with other entities in the population. DEA calculates an ―effici ent frontier‖ uses mathematical programming [15]. A benchmark, against which the comparative performance of all other firms or organizations that does not lie on the frontier can be judged, is created
@ 2010 http://www.ijaest.iserp.org. All rights Reserved.
Page 23
Shafali Jain et al. / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES Vol No. 1, Issue No. 1, 023  029
TABLE I Input
Output
Units generated Energy sold
A
Installed capacity coal consumption oil consumption auxiliary consumption energy losses
IJ
In this methodology, efficiency can be evaluated either on an inputoriented or outputoriented basis. For this paper, an inputoriented or inputminimizing approach was chosen since the purpose of the analysis was to suggest benchmarks for efficiency and reduction of inputs chosen in order to produce a given output. There can be two DEA models: CCR and BCC model and both of these models are applied in this analysis. The CCR model was suggested by Charnes et al. (1978), and hence is named as CCR model and assumes constant returns to scale (CRS) assumption. If assuming data on K inputs and M outputs for each of N firms, then for the ith firm these are represented by the column vectors xi and yi respectively. The K×N input matrix, X, and the M×N output matrix, Y, represent the data for all N firms. A measure of the ratio of all outputs over all inputs would be obtained for each firm, such as uˈyi /vˈxi, where u is an M×1 vector of output weights and v is a K×1 vector of input weights [15]. The optimal weights are obtained by solving the mathematical programming problem: maxu,v (uˈyi /vˈxi), st uˈyj /vˈxj ≤ 1, j =1,2,….N, u,v ≥ 0. (1) It is required to calculate values of u and v, such that the efficiency measure for the ith firm is maximized, subject to ISSN: 22307818
T
the constraints that all efficiency measures must be less than or equal to one. The difficulty in this ratio formulation is that it has an infinite number of solutions. This can be avoided by imposing the constraint vˈxi = 1, which provides: maxµ,v (µˈyi ), st vˈxi = 1, µˈyj  vˈxj ≤ 0, j =1,2,….N, µ,v ≥ 0, (2) where the notation is changed from u and v to µ and v, to stress that this is a different linear programming problem. Equation (2) is known as the multiplier form of the DEA linear programming problem. By the duality in linear programming, equivalent envelopment form of this problem can be derived as: minθ,λ θ , st yi + Yλ ≥ 0, θ xi – Xλ ≥ 0, λ ≥ 0, (3) where θ is a scalar and λ is a N×1 vector of constants. The efficiency score for the ith firm will be the value of θ According to the Farell (1957) definition, it will satisfy: θ ≤ 1, with a value of 1 indicating a point on the frontier and hence the firm is technically efficient firm.
ES
through this frontier. The efficient frontier is formed from the observed performances of the participating firms in the sample, determined by the relationships between the inputs and outputs of the firms in the sample. The technique was suggested by Charnes, Cooper and Rhodes and is built on the idea of Farrell [16]. There can be a number of input/output variables for evaluating the efficiency of electric utilities. The most important job in this efficiency analysis is the right selection of inputs and outputs. No universally applicable rational template is available for selection of variables [1]. In the context of efficiency measurement, the inputs must reflect the resources used and the outputs chosen must represent the activity levels of the utilities. . A study of standard literature reveals significant insights into the choice of variables. The most widely used variables based on international experience have been outlined in the literature. . Input variables chosen for DEA model are: installed capacity (MW), coal consumption (Million tonnes), oil consumption (Kilo litres), auxiliary consumption (GWh) ,energy losses (GWh) and the outputs are units generated (GWh) and energy sold (GWh) as shown in Table I.
If the utilities do not perform at optimal scales, this CCR model can be modified to take into account variable returns to scale (VRS) conditions by adding a convexity constraint. BCC model was suggested by Banker, Charnes and Cooper (1984) investigates whether the performance of each DMU was conducted in region of increasing, constant or decreasing returns to scale in multiple outputs and multiple inputs situations. The CCR efficiency can be decomposed into the Pure technical and scale efficiency components by this BCC model, thus investigating the scale effects. According to this model an inefficient firm is only ―ben chmarked‖ against firms of a similar size. The CRS linear programming problem can be easily modified to account for VRS by adding the convexity constraint: N1ˈλ=1 to (3) to provide: minθ,λ θ , st yi + Yλ ≥ 0, θ xi – Xλ ≥ 0, N1ˈλ=1 λ ≥ 0, (4) where N1 is an N×1 vector of ones. This approach forms a convex hull of interesting planes which envelope the data points more tightly than the CRS conical hull and thus provides technical efficiency scores which are greater than or equal to those obtained using the CRS model. The VRS specification has been the most commonly used specification in the 1990s. III. DATA COLLECTION AND COMPILATION DEA was used to derive the benchmarks based on the comparison of the 30 SOEUs in which 8 entities were the SEBs, 7 entities comprised various electricity departments (EDs), and 15 entities comprised the unbundled SOEUs. The
@ 2010 http://www.ijaest.iserp.org. All rights Reserved.
Page 24
Shafali Jain et al. / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES Vol No. 1, Issue No. 1, 023  029
physical data for various states were obtained for the different years from ―G eneral Review‖ published by CEA [11]. Descriptive statistics of the data for year 200708 is presented in Table II in the form of mean, median, standard deviation, minimum and maximum values. To increase the validity of the proposed model, the assumption of the ―i sotonicity‖ relationship, i.e. an increase in an input must not correspond with a decrease in an output, was examined amongst the input and output variables using correlations [1]. TABLE II
Installed Capacity Coal Consumptio n Oil Consumptio n Auxiliary Consumptio n
Mean
Median
Standard Deviation
Min
Max
3256.29
1754.05
3809.6
30.67
14580.46
6640.3
926
9444.78
0
39385
96543.91
8848
239836.16
0
1124510
910.78
195.94
1240.48
6201.84
4344.56
Units Generated
14477.57
5427.61
Energy Sold
16547.84
10956.17
0
4704.08
6661.42
129.7 4
28827.76
18407.74
21.08
72770.46
169.5 1
67930.96
A
Energy Losses
ES
Variables
18604.46
1) Efficiency Scores CCR model measures the overall efficiency which is the efficiency measured against the CRS frontier. The results are presented in Table IV. It is evident from Table VI that Indian Electric Generation Utilities display significant variations in efficiency levels. The total efficiency had a mean score of 84.83 % for all the utilities and nearly one third of utilities lie below this average value. Eleven utilities turned out to be the best practices. The remaining 19 utilities exhibited varying degree of inefficiencies. It is also observed that all the utilities, with the exception of the best practices and five utilities – Sikkim, Assam, Manipur, Arunachal Pradesh and Mizoram, exhibited decreasing returns to scale suggesting that the utilities exceeded their most productive scale size. This outcome supports the unbundling policy of the GoI, as envisaged in the Electricity Act. Five Utilities –Sikkim, Assam, Manipur, Arunachal Pradesh and Mizoram, exhibited increasing returns to scale, which indicates that these utilities are smaller than the most productive scale size. The management of the utilities, in general, does not have control over their scale of operation. Therefore, it is quite appropriate to assess efficiency relative to the VRS frontier. So, the technical efficiency of utilities is measured against the VRS frontier. To explore the scale effects, the BCC formulation that assumes a VRS by taking into consideration the sizes of utilities was employed. This formulation ensures that similar sized utilities are benchmarked and compared with each other. The results are presented in Table IV. The number of utilities that appear as efficient entities increased to 24, while remaining 6 utilities showed inefficiencies. The average technical efficiency is 97.9 %. The results indicate the possibility of restructuring of several utilities that display low scale efficiencies (Table IV). The low value of scale efficiencies and the fact that these utilities exhibit decreasing returns to scale indicate that these have considerable scope for improvements in their efficiencies by resizing (downsizing) their scales of operations to the optimal scale defined by more productive utilities in the sample.
T
DESCRIPTIVE STATISTICS
IV. ANALYSIS OF THE RESULTS
IJ
The results indicate that the variables do not isotonicity assumption. The values of correlation (Table III) indicate that the variables are correlated: neither too less of correlation nor correlation.
violate the coefficients reasonably too high a
TABLE III INPUT/OUTPUT CORRELATIONS
Variables
Installed Capacity
ISSN: 22307818
Installed Capacity
Coal consumption
Oil consumption
Auxiliary Consumption
Energy Losses
Units Generated
Total Energy Sold
1
@ 2010 http://www.ijaest.iserp.org. All rights Reserved.
Page 25
Shafali Jain et al. / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES Vol No. 1, Issue No. 1, 023  029
Coal Consumption
0.939243
1
Oil Consumption
0.800981
0.790358
1
Auxiliary Consumption
0.946876
0.99216
0.753904
1
Energy Losses
0.898126
0.940558
0.746714
0.929716
1
Units Generated
0.988273
0.956928
0.813343
0.965917
0.92134
1
Energy Sold
0.977838
0.935366
0.765371
0.94689
0.92744
0.974358
1
TABLE IV RESULTS OF CCR AND BCC MODEL
Utility
Total efficiency
Technical efficiency
Scale efficiency
Returns to scale
Benchmarks
T
S.No.
Haryana
0.682
0.826
0.825
DRS
2
Himachal Pradesh
1
1
1

2
3
Jammu & Kashmir
1
1
1

3
4
Punjab
0.969
1
0.969
DRS
4
5
Rajasthan
0.846
1
0.846
DRS
5
6
Uttar Pradesh
0.644
1
0.644
DRS
6
7
Uttrakhand
8
Delhi
9
Gujarat
10
Madhya Pradesh
0.668
11
Chhattisgarh
0.774
12
Maharashtra
0.855
13
Goa
14
Andhra Pradesh
0.88
15
Karnataka
0.909
16
Kerala
17
Tamil Nadu
18
Puducherry
19
Bihar
20
Jharkhand
21
Orissa
22
West Bengal
23
Sikkim
24
Assam
25
Manipur
26
1 0.837 1
1
1
9 4 5 18 8
1
1

7
1
0.837
DRS
8
1
1

9
0.818
0.817
DRS
8 16 9 4 21
0.805
0.961
DRS
4 9 15 26
1
0.855
DRS
12
1
1

13
1
0.88
DRS
14
1
0.909
DRS
15
1
1

16
1
0.835
DRS
17
A
0.835
ES
1
1
1

18
1
1
1

19
0.508
0.941
0.54
DRS
16 18 2 8 4
0.862
1
0.862
DRS
21
0.885
1
0.885
DRS
22
0.787
1
0.787
IRS
23
0.884
1
0.884
IRS
18 2
0.485
0.994
0.488
IRS
13 27 18
Meghalaya
1
1
1

26
27
Nagaland
1
1
1

27
28
Tripura
1
1
1

28
29
Arunachal Pradesh
0.751
0.985
0.763
IRS
2 13 27
30
Mizoram
0.388
1
0.388
IRS
30
IJ
1
2) Slack Analysis The piecewise linear form of the nonparametric frontier in DEA can cause a few difficulties in efficiency measurement. The problem arises because of the sections of the piecewise linear frontier that run parallel to the axes that do not occur in most parametric functions [15]. In such cases, even the efficient point is on frontier, one can reduce the amount of the input used and still produce the same output. After the slack evaluation, directions for improvement of the relatively ISSN: 22307818
inefficient units can be carried out. For this purpose BCC model has been used. The slack analysis results are shown in Table V in which only input slacks are shown, as inputoriented approach is used in this paper only input slacks are mentioned, as the model used in this paper is inputoriented. It is evident that slacks for efficient utilities with an efficiency score of 100 % are obviously zero. Even inefficient utilities, the slack
@ 2010 http://www.ijaest.iserp.org. All rights Reserved.
Page 26
Shafali Jain et al. / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES Vol No. 1, Issue No. 1, 023  029
values might not be present. There are 3 DMUs having slack in the installed capacity, 4 having slack in coal consumption, 1 in oil consumption, 3 in auxiliary consumption and 4 DMUs have input slack in energy losses. The results shown in the Table IV shows that some of the utilities are technically inefficient, which indicates excess resources are used by them than required to produce the given level of output. Slack evaluation for the input variables is carried out to determine the amount of inefficiencies.
S.No.
Utility
3) Evaluation of target values For each inefficient utility target value for input variable is calculated so as to make them efficient and shown in the
TABLE V SLACK ANALYSIS
Slack 1 (Installed capacity)
Slack 2 (Coal consumption)
Slack 3 (Oil consumption)
Slack 4 (auxiliary consumption)
Slack 5 (energy losses)
Haryana
0
247.534
0
0
0
2
Himachal Pradesh
0
0
0
0
0
3
Jammu & Kashmir
0
0
0
0
0
4
Punjab
0
0
0
0
0
5
Rajasthan
0
0
0
0
0
6
Uttar Pradesh
0
0
0
0
0
7
Uttrakhand
0
0
0
0
0
8
Delhi
0
0
0
0
0
9
Gujarat
0
0
0
0
0
10
Madhya Pradesh
0
2391.401
0
0
2899.024
11
Chhattisgarh
0
2001.218
0
82.782
0
12
Maharashtra
0
0
0
0
0
13
Goa
0
0
0
0
0
14
Andhra Pradesh
0
0
0
0
0
15
Karnataka
0
0
0
0
0
16
Kerala
0
0
0
0
0
17
Tamil Nadu
0
0
0
0
0
18
Puducherry
0
0
0
0
0
19
Bihar
0
0
0
0
0
20
Jharkhand
0
772.065
0
0
0
21
Orissa
0
0
0
0
0
22
West Bengal
0
0
0
0
0
23
Sikkim
0
0
0
0
0
24
Assam
55.919
0
0
63.548
1110.468
25
Manipur
19.54
0
0
0.332
114.264
26
Meghalaya
0
0
0
0
0
27
Nagaland
0
0
0
0
0
28
Tripura
0
0
0
0
0
29
Arunachal Pradesh
14.242
0
756.182
0
93.399
30
Mizoram
0
0
0
0
0
ES
A
IJ
Table VI. The target values for installed capacity, coal consumption, oil consumption, auxiliary consumption, energy losses for Haryana, Madhya Pradesh, Chhattisgarh, Jharkhand, Manipur, and Arunachal Pradesh are lower than their respective original or actual values. Let us take the case of Haryana; the input installed capacity and coal consumption should be reduced by 17 % and 20 % respectively for making it technically efficient. The mean technical efficiency of all the utilities is 97.9 % which means utilities could reduce their inputs by 2.1 % without reducing their outputs.
ISSN: 22307818
T
1
4) Summary of Peers For each inefficient utility, DEA identifies a set of efficient utilities that form a peer group for that inefficient utility. There are 26 utilities which have efficiency score of one and are technically efficient. The optimal inputoutput mix is given by the efficient utility that forms a peer for inefficient utility [2]. For example Gujarat, Punjab, Rajasthan, Puducherry and Delhi form the peer group for Haryana. For utilities having efficiency score of one, their peers are they themselves.
@ 2010 http://www.ijaest.iserp.org. All rights Reserved.
Page 27
Shafali Jain et al. / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES Vol No. 1, Issue No. 1, 023  029
TABLE VI INPUT TARGET EVALUATION
Original values
Target values
Coal consumptio n (000'MT)
Oil consumptio n (KL)
Auxiliary Consumptio n (GWh)
Energy losses (GWh)
Installed Capacit y (MW)
Coal consumptio n (000'MT)
Oil consumptio n (KL)
Auxiliary Consumptio n (GWh)
Energy losses (GWh)
3159
7819
38534
1168.1
8924.6
2610.7
6213.7
31842
965.3
7374.8
926
0
0
6.7
1026.6
6.7
1026.6
3
Haryana Himachal Pradesh Jammu & Kashmir
625
0
1718
5.1
5070.3
4
Punjab
4861
10994
16303
1623.8
8834.2
5
Rajasthan
4519
12339
22997
1983.2
12575.6
6
Uttar Pradesh
5077
16985
63082
2244
15036.4
7
Uttrakhand
1734
0
8
Delhi
932
1718
9
Gujarat
8351
22274
10
Madhya Pradesh
4483
11999
11
Chhattisgarh
2814
7994
12
Maharashtra
14580
39385
13
Goa
78
0
14
Andhra Pradesh
9452
17587
15
Karnataka
7625
7875
16
Kerala
2287
0
17
Tamil Nadu
10606
17476
18
Puducherry
32
0
19
Bihar
590
134
20
Jharkhand
1754
3796
21
Orissa
22
West Bengal
23
Sikkim
24
Assam
25
Manipur
26
Meghalaya
27 28
2
Utility
926.3
0
0
625.7
0
1718
5.1
5070.3
4861.3
10994
16303
1623.8
8834.2
4519.6
12339
22997
1983.2
12575.
5077.4
16985
63082
2244
15036.4 2624.5
0
17.82
2624.5
1734.8
0
0
17.8
11901
315.8
6556.3
932.4
1718
11901
315.8
6556.3
479022
3166.6
15650.8
8351.3
22274
479022
3166.6
15650.8
32274
1348.3
13066
3668.4
7425.2
26403
1103
7790.5
24136
926.5
4503
2265.8
4434.6
19431
663.1
3625.3
1124510
4704
28827.7
14580.4
39385
1124510
4704
28827.7
ES
1
64247
7.19
684.8
78.05
0
64247
7.1
684.8
35782
2491.1
14110.8
9452
17587
35782
2491.1
14110.8
201647
1347.3
7960.9
7625.9
7875
201647
1347.3
7960.9
112394
64.1
2554.2
2287
0
112394
64.1
2554.2
621024
2401.4
12187.4
10606.2
17476
621024
2401.4
12187.4
0
16.3
129.7
32.52
0
0
16.3
129.7
4
3.7
4186
590.4
134
4
3.7
4186
5795
453.4
3432.4
1650.3
2799.5
5452
426.6
3229.6
A
S.No.
T
Installed Capacity (MW)
2650
1889
330.7
7358.9
2498.4
2650
1889
330.7
7358.9
6590
18184
36268
2610.3
7100.7
6590
18184
36268
2610.3
7100.7
44
0
22
0.01
151.5
44.11
0
22
0
151.5
446
0
0
76
1599.2
390.3
0
0
12.5
488.8
50
0
384
0
344.2
31
0
381
0.6
227.9
189
0
0
2.1
538.7
189
8
0
0
2.14
Nagaland
30
0
0
0.02
228.8
30.6
0
0
0.02
228.8
148
0
0
8.7
297.8
148.3
0
0
8.7
297.8
29
Tripura Arunachal Pradesh
61
0
1745
0.2
347.3
45.9
0
962
0.2
248.7
30
Mizoram
69
0
639
0
144.6
69.3
0
639
0
144.6
IJ
2498
ISSN: 22307818
@ 2010 http://www.ijaest.iserp.org. All rights Reserved.
Page 28
Shafali Jain et al. / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIES Vol No. 1, Issue No. 1, 023  029
REFERENCES
[14]
[15] [16] [17] [18]
[19] [20] [21]
Tripta Thakur, S.G. Deshmukh, and S.C. Kaushik, .― Efficiency Evaluation of The State Owned Electric Utilities In India‖, Energy Policy, 34(17), 11871198, 2007. [2] D.K. Jha & R. Shrestha, ―Measuring Efficiency of Hydropower plants in Nepal using Data Envelopment Analysis‖ , IEEE Transactions on Power Systems, Vol. 21 , No 4 ,pp 15021511, November 2006. [3] M.Saleem, ― Technical Efficiency in Electricity Sector of PakistanThe impact of Private and Public Ownership.‖, PhD. [4] Tripta Thakur, S.G.Deshmukh, S.C.Kaushik, and Mukul Kulshrestha, ― Impact assessment of the Electricity Act 2003 on the Indian power sector.‖, Energy Policy, vol. 33, no. 9, pp. 11871198, 2005. [5] MoP , 2009. Ministry of Power website, http://powermin.nic.in/ [6] K.P.Kannan, N.V.Pillai, 2000. Plight of the Power Sector in India: SEBs and Their saga of inefficiency. Working Paper No. 308, November 2000. Centre For Development Studies, thiruvananthapuram. [7] D.K. Jha, N.Yorino ,and Y.Zoka, ― A Modified DEA Model for Benchmarking of Hydropower Plants.‖, PowerTech 2007 [8] P.Chitkara, ― A Data Envelopment analysis Approach to Evaluation of Opeartional Inefficiencies in Power Generating Units: A Case Study of Indian Power Plants.‖ IEEE Transactions on Power Systems, Vol. 14, no. 2, May 1999. [9] B. Golany, Y. Roll, and D. rybak,‖Measuring Efiiciency of Power Plants in Israel by Data Envelopment Analysis.‖ IEEE Transactions on Engineering Managrment, vol. 41, no. 3, pp. 291301, Aug. 1994. [10] A. Vaninsky, ― Efficiency of electric power generation in the United States: Analysis and forecast based on data envelopment analysis.‖, Energy Economics, vol. 28, pp. 326338, 2006. [11] All India Electricity Statistics , General Review 2009, Central Electricity Authority, New Delhi.
IJ
A
[1]
[13]
ES
The mean CRS and VRS efficiencies are 84.8 % and 97.9 % respectively. All the utilities, with the exception of the best practices and and five Utilities –Sikkim, Assam, Manipur, Arunachal Pradesh and Mizoram, exhibited decreasing returns to scale suggesting that the utilities exceeded their most productive scale size. The numbers of utilities that appear as efficient entities are 11 in case of CRS while under VRS condition, it increased to 24. This VRS formulation ensures that similar sized utilities are benchmarked and compared with each other. It is evident that slacks for efficient utilities with an efficiency score of 100 % are obviously zero. The slack values might not be present even for inefficient utilities. There are 3 DMUs having slack in the installed capacity, 4 having slack in coal consumption, 1 in oil consumption, 3 in auxiliary consumption and 4 DMUs have input slack in energy losses. For each inefficient utility target value for input variable is calculated so as to make them efficient. The target values for installed capacity, coal consumption, oil consumption, auxiliary consumption, energy losses for Haryana, Madhya Pradesh, Chhattisgarh, Jharkhand, Manipur, and Arunachal Pradesh are lower than their respective original or actual values. The mean technical efficiency of all the utilities is 97.9 % which means utilities could reduce their inputs by 2.1 % without reducing their outputs.
M. Abbott, ― The productivity and efficiency of the Australian electricity supply industry.‖ ,Energy Economics, vol. 28, pp. 444338, 2006. K. sarica and I. Or, ― Efficiency assessment of Turkish power plants using data envelopment analysis.‖ ,Energy, vol. 32, pp. 14841499, 2007. R. F. Lovado, ― Benchmarking the efficiency of Philippines Electric Cooperatives Using Stochastic Frontier Analysis and Data Envelopment Analysis‖,Third East West Center International Graduate Student Conference, Hawaii, Feb. 2004. T. Coelli, D.S. Prasado Rao, and George E. Battese, ― An Introduction to Efficiency and Productivity Analysis.‖ W.W. Cooper and K. Tone, ― Measures of inefficiency in data envelopment analysis and stochastic frontier estimation.‖, European Journal of Operational Research, 99(7288), 1997. A. Charnes, W.W. Cooper and E. Rhodes, ― Mesauring the efficiency of decision making units‖, European Journal of Operational Research, vol. 2, no. 6, pp 429444. R. Meenakumari and N. Kamraj, ― Measurement of Relative Efficiency of State Owned Electric Utilities in India Using Data Envelopment analysis.‖, Modern Applied Science, vol. 2, no. 5 , pp 6171, Sep 2008. V.K.Yadav, N.P. Padhy, and H.O.Gupta, ― Assessing the performance of electric utilities of developing countries: An intercountry comparison using DEA‖, IEEE Transaction. A. Pahwa, X. Feng, and d. Lubkeman, ― Performance evaluation of electric distribution utilities based on data envelopment analysis.‖ IEEE Transactions on Power Systems, Vol. 18, no. 1, pp. 400405, Feb 2003. Tripta Thakur, ― Benchmarking study for the Indian Electric Electric utilities Data Envelopment Analysis‖, IEEE Transactions on Power Systems, pp 545549, 2005.
T
[12]
V. CONCLUSIONS
ISSN: 22307818
@ 2010 http://www.ijaest.iserp.org. All rights Reserved.
Page 29